Care must be taken that research participates in the cumulative science of behavior change?
Bibliographic record
Abstract
We read with great interest the article by Şekerci and Kitiş [1] describing a Transtheoretical Model (TTM) based intervention delivered by motivational interviews to improve physical exercise in women with diabetes. Although the TTM has been regularly criticized, the Şekerci and Kitiş [1] study illustrates that TTM interventions are effective to improve physical activity (PA) in adults. However, based on their investigation, we would like to highlight three points. First, this study illustrates that TTM-based interventions to promote PA in clinical practices are effective but, second, the implementation is generally not based on the revised TTM assumptions. Last, from a research perspective, the lack of details and information provided about the intervention represent an important obstacle to future reproducibility. It is now clear that theory-based interventions, including those based on the TTM, improve PA. Indeed, Gourlan et al. [2] found an overall significant effect for TTM-based interventions (31 randomized controlled trials) with a medium effect size (d = .31, 95% confidence interval: 0.11–0.32). This finding was confirmed in an updated meta-analysis in 2018 [3]. Nevertheless, and as confirmed by previous systematic investigations, most of TTM interventions are TTM inspired rather than TTM based. Therefore, it suggests that TTM-based interventions should be carefully implemented [4,5].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.047 | 0.049 |
| Insufficient payload (model declined to judge) | 0.021 | 0.025 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".